The Trends and the Drivers of Deforestation

A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies

Research output: Book chapter/Published conference paperChapter (peer-reviewed)

7 Downloads (Pure)

Abstract

Policies for Reducing Emissions from Deforestation and Forest Degradation, known as REDD, and enhancing forest carbon stocks, known as REDD+, could provide a way for tackling global warming and climate change. In this regard several proposals were designed, yet their implementation poses significant methodological problems. One of those problems can be the interactions between the direct and indirect causes (drivers) of deforestation. Deforestation is a transformation of forestland for various land uses. This chapter therefore analyses trends in world deforestation in relation to different geographical regions and its drivers. A cross-sectional econometric model, recursive in nature, is estimated in two stages for addressing the interaction between the causes. Firstly, the direct causes of deforestation are regressed on indirect causes, by Seemingly Unrelated Regression (SUR) estimation to account for the correlations between the direct causes. Secondly, the SUR estimates of the direct causes are used for the regression of deforestation equation. The statistical evidences show prevalence of omitted variables for the indirect causes, as well as correlations between the direct causes. The SUR estimates are therefore efficient than OLS estimates. The results are discussed, in relation to Asian, African and Latin American regions, to provide guidance for designing effective REDD+ policies.
Original languageEnglish
Title of host publicationDeforestation
Subtitle of host publicationConservation Policies, Economic Implications and Environmental Impact
EditorsCarlos Narciso Bouza Herrera
Place of PublicationUnited States
PublisherNova Science Publishers
Chapter3
Pages81-100
Number of pages20
ISBN (Print)9781629482415
Publication statusPublished - 2013

Fingerprint

deforestation
regression analysis
geographical region
econometrics
trend
policy
global climate
global warming
land use
degradation
climate change
carbon

Cite this

Culas, R. (2013). The Trends and the Drivers of Deforestation: A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies. In C. N. B. Herrera (Ed.), Deforestation: Conservation Policies, Economic Implications and Environmental Impact (pp. 81-100). United States: Nova Science Publishers.
Culas, Richard. / The Trends and the Drivers of Deforestation : A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies. Deforestation: Conservation Policies, Economic Implications and Environmental Impact. editor / Carlos Narciso Bouza Herrera. United States : Nova Science Publishers, 2013. pp. 81-100
@inbook{23b1609c627047f1b4edfe3df9984c3b,
title = "The Trends and the Drivers of Deforestation: A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies",
abstract = "Policies for Reducing Emissions from Deforestation and Forest Degradation, known as REDD, and enhancing forest carbon stocks, known as REDD+, could provide a way for tackling global warming and climate change. In this regard several proposals were designed, yet their implementation poses significant methodological problems. One of those problems can be the interactions between the direct and indirect causes (drivers) of deforestation. Deforestation is a transformation of forestland for various land uses. This chapter therefore analyses trends in world deforestation in relation to different geographical regions and its drivers. A cross-sectional econometric model, recursive in nature, is estimated in two stages for addressing the interaction between the causes. Firstly, the direct causes of deforestation are regressed on indirect causes, by Seemingly Unrelated Regression (SUR) estimation to account for the correlations between the direct causes. Secondly, the SUR estimates of the direct causes are used for the regression of deforestation equation. The statistical evidences show prevalence of omitted variables for the indirect causes, as well as correlations between the direct causes. The SUR estimates are therefore efficient than OLS estimates. The results are discussed, in relation to Asian, African and Latin American regions, to provide guidance for designing effective REDD+ policies.",
keywords = "Open access version available, Climate change, Drivers of deforestation, REDD+, SUR, Two-stage estimation",
author = "Richard Culas",
note = "Imported on 12 May 2017 - DigiTool details were: publisher = United States: Nova Science, 2013. editor/s (773b) = Carlos Narciso Bouza Herrera; Issue no. (773s) = 3; Parent title (773t) = Deforestation: Conservation Policies, Economic Implications and Environmental Impact.",
year = "2013",
language = "English",
isbn = "9781629482415",
pages = "81--100",
editor = "Herrera, {Carlos Narciso Bouza}",
booktitle = "Deforestation",
publisher = "Nova Science Publishers",
address = "United States",

}

Culas, R 2013, The Trends and the Drivers of Deforestation: A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies. in CNB Herrera (ed.), Deforestation: Conservation Policies, Economic Implications and Environmental Impact. Nova Science Publishers, United States, pp. 81-100.

The Trends and the Drivers of Deforestation : A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies. / Culas, Richard.

Deforestation: Conservation Policies, Economic Implications and Environmental Impact. ed. / Carlos Narciso Bouza Herrera. United States : Nova Science Publishers, 2013. p. 81-100.

Research output: Book chapter/Published conference paperChapter (peer-reviewed)

TY - CHAP

T1 - The Trends and the Drivers of Deforestation

T2 - A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies

AU - Culas, Richard

N1 - Imported on 12 May 2017 - DigiTool details were: publisher = United States: Nova Science, 2013. editor/s (773b) = Carlos Narciso Bouza Herrera; Issue no. (773s) = 3; Parent title (773t) = Deforestation: Conservation Policies, Economic Implications and Environmental Impact.

PY - 2013

Y1 - 2013

N2 - Policies for Reducing Emissions from Deforestation and Forest Degradation, known as REDD, and enhancing forest carbon stocks, known as REDD+, could provide a way for tackling global warming and climate change. In this regard several proposals were designed, yet their implementation poses significant methodological problems. One of those problems can be the interactions between the direct and indirect causes (drivers) of deforestation. Deforestation is a transformation of forestland for various land uses. This chapter therefore analyses trends in world deforestation in relation to different geographical regions and its drivers. A cross-sectional econometric model, recursive in nature, is estimated in two stages for addressing the interaction between the causes. Firstly, the direct causes of deforestation are regressed on indirect causes, by Seemingly Unrelated Regression (SUR) estimation to account for the correlations between the direct causes. Secondly, the SUR estimates of the direct causes are used for the regression of deforestation equation. The statistical evidences show prevalence of omitted variables for the indirect causes, as well as correlations between the direct causes. The SUR estimates are therefore efficient than OLS estimates. The results are discussed, in relation to Asian, African and Latin American regions, to provide guidance for designing effective REDD+ policies.

AB - Policies for Reducing Emissions from Deforestation and Forest Degradation, known as REDD, and enhancing forest carbon stocks, known as REDD+, could provide a way for tackling global warming and climate change. In this regard several proposals were designed, yet their implementation poses significant methodological problems. One of those problems can be the interactions between the direct and indirect causes (drivers) of deforestation. Deforestation is a transformation of forestland for various land uses. This chapter therefore analyses trends in world deforestation in relation to different geographical regions and its drivers. A cross-sectional econometric model, recursive in nature, is estimated in two stages for addressing the interaction between the causes. Firstly, the direct causes of deforestation are regressed on indirect causes, by Seemingly Unrelated Regression (SUR) estimation to account for the correlations between the direct causes. Secondly, the SUR estimates of the direct causes are used for the regression of deforestation equation. The statistical evidences show prevalence of omitted variables for the indirect causes, as well as correlations between the direct causes. The SUR estimates are therefore efficient than OLS estimates. The results are discussed, in relation to Asian, African and Latin American regions, to provide guidance for designing effective REDD+ policies.

KW - Open access version available

KW - Climate change

KW - Drivers of deforestation

KW - REDD+

KW - SUR

KW - Two-stage estimation

M3 - Chapter (peer-reviewed)

SN - 9781629482415

SP - 81

EP - 100

BT - Deforestation

A2 - Herrera, Carlos Narciso Bouza

PB - Nova Science Publishers

CY - United States

ER -

Culas R. The Trends and the Drivers of Deforestation: A Cross-country Seemingly Unrelated Regression Analysis for the REDD+ Policies. In Herrera CNB, editor, Deforestation: Conservation Policies, Economic Implications and Environmental Impact. United States: Nova Science Publishers. 2013. p. 81-100